Inviting AI into your classroom and simply saying, “Use it responsibly,” is like inviting a wacky stranger to your dinner party and saying, “Just don’t make it weird.”

Nobody knows what that means. You turn your back for five minutes to grab the wine, and you come back to find the stranger reorganizing your spice rack by scent and trying to sous-vide the family cat. Everyone is staring at you, and you’re left scratching your head, trying to reverse-engineer the chaos from the mess.

That’s the problem with vague instructions: they invite interpretation. As Katie Novak points out in the foreword to the forthcoming second edition of my book, AI-Enhanced Processes, being a host isn’t just about opening the front door; it’s an intentional design. In a classroom, we’re hosting a digital guest in a room full of humans. If we don’t facilitate the introduction, the humans don’t know how to engage, and the guest, with all its enthusiasm and desire to please, takes over the party learning experiences.

That’s basically the current state of AI school policies. Often, they're labels that sound decisive but rely on conceptual language. Many teachers are still unsure how to put that language into practice. Don't get me wrong, schools need a policy that establishes a shared language for how stakeholders will engage with AI in classrooms. However, action plans, practice, professional development, and coaching are the necessary next steps. Without the practical implementation plan, we likely will encounter selective interpretation: a teacher thinks “use AI only for brainstorming” when indicating AI is allowed. A student hears “drafting with AI is fine.” A school leader hears “future-ready learning.”

We need a better way to host AI as a guest in our classrooms, and that means practicing our shared agreements, not just writing them.

That’s where an AI-enhanced process (AIEP) comes in as one meaningful strategy to operationalize good practices. AIEPs are short, repeatable sequences of thinking moves that make expectations concrete. They keep the effort with the learner and give AI a clear role without letting it take over. Instead of “AI allowed,” we get shared language for action: here are the thinking moves, here’s how AI can collaborate on each one, and here’s what stays human.

A strong AI-enhanced process includes these facets by design:

* Multiple steps that break a complex task into smaller moves, so learning happens through a sequence.

* A variety of action-oriented thinking moves (generate, analyze, evaluate, reflect) that build toward a larger outcome.

* Visible thinking through drafts, notes, prototypes, and reflections so the work can be coached while it’s still happening. Process journals, folios, photos, Padlets, to name a few, are great ways to document student learning. It’s most powerful when it’s for the students first, and adults second. I will share more about this below, but I also have a two-article series you might enjoy. They touch on big ideas like efficiency and effort. Links below.

* Explicit roles so each step clarifies what students do and how AI can collaborate without taking over like our wacky guest at the dinner party.

* Embedded AI literacy happens through classroom dialogue and intentionally designed learning experiences, where AI shows up in ways that actually enhance learning. Students learn by doing. They build meaningful habits with AI when lessons use it to strengthen thinking, not replace it. Reflection helps students notice how the process actually flowed for them: where AI supported their growth, where it got in the way, and what they want to adjust next time. Over time, that cycle of practice and reflection becomes a lifelong strategy that students can carry into adulthood.

In summary, AI-enhanced processes are strategies we intentionally design in conjunction with meaningful classroom dialogue about how we use AI with our students, which can lead to powerful learning experiences and lifelong strategies.

Ok. Now that we have shared language for what an AIEP is, we can actually host AI in our classrooms. In the next sections, we’ll look at three powerful moves to build your own processes that take the above ideas into account and operationalize them.

* Powerful Move 1: Name The Thinking

* Powerful Move 2: Set Expectations around Student-AI Collaboration

* Powerful Move 3: Make Learning Visible

Powerful Move 1: Name The Thinking

Naming the thinking in a process is an idea borrowed from Project Zero’s work around making thinking visible. And in that sense, this is not really a new idea; it’s the notion that we consider the category of thinking, and then name the verb we want students to practice. For example, the category might be to think critically, and the verb might be to evaluate. Project Zero suggests that if you care about thinking, you have to design for it. You have to name it, value it, and make it observable, because what a classroom repeatedly reinforces and rewards is what students learn to do. And with AI, if we don’t name the thinking, the easiest path of the least resistance wins: creating a polished product automatically. It’s tempting for students to skip the messy process that requires effort and to focus on task completion. If we do name it, we can build the habits of mind we actually want to instill in our students.

Another important thing to note when naming the thinking is that each thinking move is not an “activity” to keep kids busy, nor are they compliance checklists. They’re steps that make thinking visible enough to practice another powerful move: metacognition, in which we can talk about the thinking, coach for improvement, encourage students to notice their own growth, and plan their own independent next steps.

In addition to Harvard Project Zero’s Thinking Routines, there are other processes that follow very similar patterns, like Stanford D.School’s Design Thinking cycle, writing workshops, lab reports, Socratic seminars, critique protocols, coaching cycles, research, and a hundred other classroom processes that work because they support the thinking that leads to a solid product.

Below is a preview of my book with samples of words that we can draw upon. It’s not an exhaustive list, but it’s a good place to start, helping us think about what we mean by “naming the thinking moves”. Notice the bolded words under the “Example Thinking Verbs” column. Those are things that AIs can currently do quite well on their own or in collaboration with our children.

When planning, ask yourself:

* What types of thinking are important in this task?

* If I were to name what students should do as verbs, which ones come to mind?

* How can I build a shared vocabulary with my learners about what these words mean?

* When in the year is it helpful to introduce, practice, and review this language?

In the next section, we will take the named thinking moves and set expectations for when and how AI collaborates with students on each move.

Powerful Move 2: Set Expectations around Student-AI Collaboration

I like to frame AI as not a tool, not a calculator, and certainly not a threat, but a guest collaborator. That framing changes the way we introduce it to students. We’re not handing them a device and saying, “Go use this.” We’re inviting AI into the learning space, and we should be deliberate about the roles it plays.

For example, if the thinking move is brainstorming, you might say: “AI will collaborate by asking Socratic questions that push your creativity. It will not generate ideas or suggestions for you.” Now students know what they’re responsible for, what AI is responsible for, and what’s off-limits.

You can take that one step further by narrowing which AI students use. In class, that might sound like: “I built a bot for you to use that’s aware of our learning objectives. Please use this bot only. It helps me be a better teacher, it allows me to coach you with more precision, and it makes your AI use transparent so we’re both clear on when and how you leveraged AI to support your effortful thinking.”

From there, the planning move is straightforward: decide how AI supports each thinking move based on your knowledge of the learning goal and your students. Caveat: this kind of decision making does require some AI literacy from us as teachers, enough familiarity to predict what the tool will do well and where it might take over the thinking.

If you’re new to it, AI-use scales can make expectations easier to name and share. You can use an existing one or build your own.

One option is the Expectations Scale that Holly Clark and I created. It helps you label what AI can and cannot do within each step of a process. For example: “During empathizing, you may use AI to give feedback on the depth of your interview questions,” or “During outlining, you may use AI to help you structure your plan.”

Another option is the AI Assessment Scale by Perkins et al. It’s widely used and has influenced a lo...